From Open Data to Open Governance in Canada: Dissecting a Work in Progress
Bibliographic record
Abstract
Many governments are striving to develop open data strategies, taking previously internal and often proprietary sources of information and rendering them public through online spaces. However, open data does not fit easily within the rubric of democratic governance and traditional public administration. The purpose of this chapter is to probe such tensions within the context of Canada, a Parliamentary democratic regime of the Westminster tradition where the inertia of the machinery of government often translates into a penchant for informational control rather than openness and sharing. More specifically, we examine the federal government’s ongoing Open Government Action Plan and its three main dimensions: data, information and dialogue. Within each dimension, there are tensions between opportunities and pressures for openness and sharing on the one hand, and the inertia of traditional government and proprietary notion of information ownership and control on the other hand. Within a broader democratic context as well, notions of individual privacy coexist uneasily with the emerging culture of openness and sharing, a culture greatly facilitated by the advent of mobile computing and devices. This chapter concludes with a call for greater political innovation and dialogue in order to facilitate a more meaningful path of institutional adaption predicated upon enlightened openness and data sharing aligned with a culture of responsible and genuine public involvement in the creation of public value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.036 | 0.034 |
| Scholarly communication | 0.032 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".